There are lots of redundant information in DNNs (Deep Neural Networks), including the number and precision of parameters, which leaves us great opportunities to perform many algorithm optimizations. With our world-leading research in neural network model compression, DeePhi developed DECENT (DEep ComprEssioN Tool). It innovative introduces pruning, quantization, weight-sharing and Huffman encoding to reduce model size from 5x to 50x without loss of accuracy. Therefore, it greatly brings DPU platform higher computation efficiency, better energy efficiency and lower system memory bandwidth requirement.